word-embedding

Create word-embedding model using skip-gram and negative sampling. This model is trained with wikipedia and opensubtitle corpus with total word = 270M and output feature dimension = 256.

Requirements to use this program

---

• Python 2 or 3
• Gensim
• sklearn
• w2v-model

To visualizing word vector using T-SNE, run :

---

$ python tsne_plot.py list_word

This will generate a ~500 word with list_word as a generating keyword, and create a 2D plot of word vector.

Example :

---

$ python plot-tsne.py siang komputer sendu kaki mati apel relativitas emansipasi jokowi

Plot example :

---

Corpus source :

Wikipedia (~5.6GB) : https://dumps.wikimedia.org/idwiki/20200101/
opensubtitle (~702MB) : http://opus.nlpl.eu/download.php?f=OpenSubtitles/v2018/mono/OpenSubtitles.raw.id.gz